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Metadata and the Semantic Web. Marlon Pierce Community Grids Lab Indiana University. Introduction. Overview things and provide some motivating examples. Introduction: What’s in data.out?. Metadata is information about other data-->resources.

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Metadata and the semantic web

Metadata and the Semantic Web

Marlon Pierce

Community Grids Lab

Indiana University



Overview things and provide some motivating examples.

Introduction what s in data out
Introduction: What’s in data.out?

  • Metadata is information about other data-->resources.

    • Typically it is a collection of property-value pairs

      • Property names established by convention

    • What is my data?

    • Where is my data?

    • When was this data created?

    • How was it created?

    • Who generated this garbage?

  • The Semantic Web attempts to define a metadata information model for the Internet to aid in information retrieval and aggregation.

    • Provides general languages for describing any metadata

    • Advanced capabilities intended to enable knowledge representation and limited machine reasoning.

  • Coupling of the Semantic Web with Web Service and Grid technologies is referred to as the Semantic Grid.

Seminar goal
Seminar Goal

  • Demonstrate the wide applicability of XML metadata representation to

    • DOD Scientists: describing data provenance, computing resources

    • Information managers: digital libraries

  • Hear from participants

    • Are you using metadata now?

    • Do you see the need for this in your research?

Semantic web vision
Semantic Web Vision

  • Well known Scientific American article by Tim Berners-Lee, James Hendler, and Ora Lassila


  • Example: making a doctor’s appointment

Semantic grid vision for hpc
Semantic Grid Vision for HPC

  • A geologist is interested in Pacific Rim earthquake events of a particular type, stored in databases in several different countries.

  • The data must be coupled to compute servers that offer particular algorithms. He also requests MPEG movies of the results.

  • Midway through the process, he decides to go home, so he preempts the MPEG generation service to just generate a set of JPEGS and requests a call to his cell phone with the images when done.

What are the characteristics
What Are the Characteristics?

  • All devices, data, algorithms, and web services are described

    • Metadata!

    • Enables discovery

  • A user’s requests are translated into instructions

    • Distributed programming languages

    • Workflow, another talk

  • Distributed components execute the workflow instructions

    • Brokers, agents, services, engines, events

    • Another talk

  • Ideally, each of these components is independent.

    • Implementation-independent metadata, workflow languages, services, and engines

  • Collectively, the data, hardware, services, etc., define the Semantic Grid.

    • Another talk

  • Need to provide a general framework for doing these kinds of tasks.

    • Go beyond demos

    • Start with the metadata

Scientific metadata

Scientific Metadata

Define metadata and describe its use in physical and computer science.

What is metadata
What is Metadata?

  • Common definition: data about data

  • “Traditional” Examples

    • Prescriptions of database structure and contents.

    • File names and permissions in a file system.

    • HDF5 metadata: describes data characteristics such as array sizes, data formats, etc.

  • Metadata may be queried to learn the characteristics of the data it describes.

  • Traditional metadata systems are functionally tightly coupled to the data they describe.

    • Prescriptive, needed to interact directly with data.

Metadata and the web
Metadata and the Web

  • Traditional metadata concepts must be extended as systems become more distributed, information becomes broader

    • Tight functional integration not as important

    • Metadata used for information, becomes descriptive.

    • Metadata may need to describe resources, not just data.

  • Everything is a resource

    • People, computers, software, conference presentations, conferences, activities, projects.

  • We’ll next look at several examples that use metadata, featuring

    • Dublin Core: digital libraries

    • CMCS: chemistry

The dublin core metadata for digital libraries
The Dublin Core: Metadata for Digital Libraries

  • The Dublin Core is a set of simple name/value properties that can describe online resources.

    • Usually Web content but generally usable (CMCS)

    • Intended to help classify and search online resources.

  • DC elements may be either embedded in the data or in a separate repository.

  • Initial set defined by 1995 Dublin, Ohio meeting.

Dublin core elements
Dublin Core Elements

  • Content elements:

    • Coverage, description, type, relation, source, subject, title.

  • Intellectual property elements:

    • Contributor, creator, publisher, rights

  • Instantiation elements:

    • Date, format, identifier, language

  • In RDF, these are called properties.

Dublin core element refinements
Dublin Core Element Refinements

  • Many of these, and extensible

  • See for the comprehensive list of elements and refinements

  • Examples:

    • isVersionOf, hasVersion, isReplacedBy, references, isReferencedBy.

Encoding the dublin core
Encoding the Dublin Core

  • DC elements are independent of the encoding syntax.

  • Rules exist to map the DC into

    • HTML

    • RDF/XML

  • We provide a detailed overview of RDF/XML encoding in this seminar.

Collaboratory for multiscale chemical science cmcs
Collaboratory for Multiscale Chemical Science (CMCS)

  • SciDAC project involving several DOE labs

    • See

  • Project scope is to build Web infrastructure (portals, services, distributed data) to enable multiscale coupling of chemical applications

Cmcs is data driven grid
CMCS Is Data Driven Grid

  • Core of the CMCS project is to exchange chemical data and information between different scales in a well defined, consistent, validated manner.

  • Journal publication of chemical data is too slow.

  • Need to support distributed online chemical data repositories.

  • Need an application layer between the user and the data.

    • Simplify access through portals and intelligent search tools.

    • Control read/write access to data

Cmcs data problems
CMCS Data Problems

  • Users need to intelligently search repositories for data.

    • Characterize it with metadata

  • Many data values are derived from long calculation chains.

    • Bad data can propagate, corrupt many dependent values.

  • Experimental values are also sometimes questionable.

  • Always the problem of incorrect data entry, errata.

Solution annotation metadata and data pedigree
Solution: Annotation Metadata and Data Pedigree

  • CMCS provides subject area metadata tags to identify data

    • Species name, Chemical Abstracts Service number, formula, common name, vibrational frequency, molecular geometry, absolute energy, entropy, specific heat, heat capacity, free energy differences, etc.

  • Data Pedigree also must be recorded.

    • Where was it published/described?

    • Who measured or calculated the values?

      • Intellectual property

    • How were the values obtained?

    • What other values does it depend upon?

  • Also provides community annotation capabilities

    • Is this value suspicious? Why?

      • Monte Carlo and other techniques exist to automate this.

    • Has the data been officially blessed? By whom?

      • Curation

Scientific annotation middleware sam approach
Scientific Annotation Middleware (SAM) Approach

  • General purpose metadata system.

  • Based on WebDAV

    • Standard distributed authoring tool.

    • See next slide

  • Uses extended Dublin Core elements to describe data.

    • Title, creator, subject, description, publisher, date, type, format, source,isResplaceBy, replaces, hasVersion isReferencedBy, hasReferences.

  • SAM available from




Electronic Notebook


Notebook Services


records mgmt., annotation


timestamps, signatures



Search &

Semantic Navigation




Components and Service Interfaces

Data Store Interface




Problem Solving


Aside 0 what is webdav
Aside #0: What is WebDAV?

  • IETF standard extension to HTTP for Web-based distributed authoring and version control.

    • Operations include put, get, rename, move, copy

    • Files are described with queryable metadata

      • Name/value pairs

      • Who is the author? What is the last revision?

    • Allows you to assign group controls

    • See many links at

  • Web Service before its time.

  • Documents for this seminar available from a WebDAV server (Slide).

  • Many commercial implementations

    • MS Web folders are just DAV clients.


  • CMCS issues are not unique to chemistry.

  • SERVOGrid is a NASA project to integrate historical, measured, and calculated earthquake data (GPS, Seismicity, Faults) with simulation codes.

  • Using GML extensions as common data format.

  • “Those 1935 measurements aren’t so good…”

Digital libraries
Digital Libraries

  • The CGL publication page is our simple “digital library”.


  • Raw material for testing tools and applications

Lab publications also maintained in xml nuggets browser
Lab Publications Also Maintained in XML Nuggets Browser

  • Developed as part of the OKC work.

  • Uses XML schemas based on bibtex for data models.

  • Provides posting, browsing, searching, and editing features.

  • Basic problem: Need a better way to link nuggets

    • Currently each application is based around a single schema or set of related schemas

    • Authors, publications, projects, glossary terms (key words) should all be navigable through a single interface.

Rib software metadata
RIB: Software Metadata

  • UTK’s RIB is designed manage software metadata.

  • Assets are key class

    • Information about software specifications, documentation, source code, etc.

  • Libraries are comprised of assets

  • Organizations own libraries and assets.

  • BIDM expressed in XML is current RIB versions.

  • BIDM extensions express

    • Asset certification: curation

    • Intellectual property: pedigree

Universal description discovery and integration uddi
Universal Description Discovery and Integration (UDDI)

  • General purpose online, integrated information repository.

    • Uses XML data models

    • Accessed through Web services (WSDL, SOAP).

  • Often used as an information repository for Web services

    • Links to WSDL and service location URLs.

  • Actually UDDI is a general purpose online business information system.

    • Can intended to store business information and classifications, contact information, etc.

Application and computer metadata
Application and Computer Metadata

  • Developed by IU to support Gateway project.

  • Describes applications (codes), hosts, queuing systems.

    • Coupled XML schemas

  • Stores information such as

    • Input and output file locations, machines used, etc.

  • Coupled with Web services to run codes, generate batch scripts, archive portal sessions.

Grid portal information repository gpir
Grid Portal Information Repository (GPIR)

  • TACC’s GPIR is an integrated information system

    • XML data models (more below)

    • Web services for ingesting, querying data

    • Portlets for displaying, interacting with info

  • Several independent XML schemas for describing computing resources

    • Static info: machine characteristics (OS, number of processors, memory) and MOTDs.

    • Dynamic info: loads, status of hosts, status of jobs on hosts, nodes on hosts.

Metadata trends and lessons
Metadata Trends and Lessons

  • Online data repositories becoming increasingly important, so need pedigree, curation, and annotation.

  • XML is the method of choice for describing metadata.

    • “Human understandable”

    • OS and application independent.

    • Provides syntax rules but does not really encode meaning

  • But there is no generic way to describe metadata.

    • How can we resolve differences in Application Metadata and GPIR, for example?

    • This should be possible, since metadata ultimately boils down to structured name/value pairs.

  • The Semantic Web tools seek to solve these problems.

Xml primer

XML Primer

General characteristics of XML

Basic xml

XML consists of human readable tags

Schemas define rules for a particular dialect.

XML Schema is the root, defines the rules for making other XML schemas.

Tree structure: tags must be closed in reverse order that they are opened.

Tags can be modified by attributes

name, minOccurs

Tags enclose either strings or structured XML

<complexType name="FaultType">


<element name="FaultName"

type="xsd:string" />

<element name="MapView/>

<element name="CartView“/>

<element name="MaterialProps" minOccurs="0" />


<element name="Slip" />

<element name="Rate" />




Basic XML

Namespaces and uris

XML documents can be composed of several different schemas.

Namespaces are used to identify the source schema for a particular tag.

Resolves name conflicts—”full path”

Values of namespaces are URIs.

URI are just structured names.

May point to something not electronically retrievable

URLs are special cases.

<xsd:schema xmlns:xsd=""xmlns:gem="”>






Namespaces and URIs

Resource description framework

Resource Description Framework

Overview of RDF basic ideas and XML encoding.

Building semantic markup languages
Building Semantic Markup Languages

  • XML essentially defines syntax rules for markup languages.

    • “Human readable” means humans provide meaning

  • We also would like some limited ability to encode meaning directly within markup languages.

  • The semantic markup languages attempt to do that, with increasing sophistication.

  • Stack indicates direct dependencies: DAML is defined in terms of RDF, RDFS.

Resource description framework rdf
Resource Description Framework (RDF)

  • RDF is the simplest of the semantic languages.

  • Basic Idea #1: Triples

    • RDF is based on a subject-verb-object statement structure.

    • RDF subjects are called classes

    • Verbs are called properties.

  • Basic Idea #2: Everything is a resource that is named with a URI

    • RDF nouns, verbs, and objects are all labeled with URIs

    • Recall that a URI is just a name for a resource.

    • It may be a URL, but not necessarily.

    • A URI can name anything that can be described

      • Web pages, creators of web pages, organizations that the creator works for,….

What does this have to do with scientific computing
What Does This Have to Do with Scientific Computing?

  • RDF resources aren’t just web pages

    • Can be computer codes, simulation and experimental data, hardware, research groups, algorithms, ….

  • Recall from the CMCS chemistry example that they needed to describe the provenance, annotation, and curation of chemistry data.

    • Compound X’s properties were calculated by Dr. Y.

  • CMCS maps all of their metadata to the Dublin Core.

  • The Dublin Core is encoded quite nicely as RDF.

Rdf graph model
RDF Graph Model

  • RDF is defined by a graph model.

  • Resources are denoted by ovals.

  • Lines (arcs) indicate properties.

  • Squares indicate string literals (no URI).

  • Resources and properties are labeled by a URI.




Encoding rdf in xml
Encoding RDF in XML

  • The graph represents two statements.

    • Entry X has a creator, Dr. Y.

    • Entry X has a title, H2O.

  • In RDF XML, we have the following tags

    • <RDF> </RDF> denote the beginning and end of the RDF description.

    • <Description>’s “about” attribute identifies the subject of the sentence.

    • <Description></Description> enclose the properties and their values.

    • We import Dublin Core conventional properties (creator, title) from outside RDF proper.

Rdf xml the gory details
RDF XML: The Gory Details

<rdf:RDF xmlns:rdf='' xmlns:dc=''>


rdf:about='http://.../X‘> <dc:creator='http://…/people/MEP‘/> <dc:title='H2O'/> </rdf:Description>


Structure of the document
Structure of the Document

  • RDF XML probably a bit hard to read if you are not familiar with XML namespaces.

  • Container structure is illustrated on the right.

  • Skeleton is


    <Description about=“”>









Aside 1 creating rdf documents
Aside #1: Creating RDF Documents

  • Writing RDF XML (or DAML or OWL) by hand is not easy.

    • It’s a good way to learn to read/write, but after you understand it, automate it.

  • Authoring tools are available

    • OntoMat: buggy

    • Protégé: preferred

    • IsaViz: looks nice, need to examine

  • You can also generate RDF programmatically using Hewlett Packard Labs’ Jena toolkit for Java.

    • This is what I did in previous example.

Aside 2 what s a purl
Aside #2: What’s a PURL?

  • As you may have deduced, RDF’s use of URIs implies the need for registering official names.

  • A PURL is a Persistent URL/URI.

    • PURLs don’t point directly to a resource

    • Instead, they point to a resolution service that redirects the client to the current URL.

    • See

  • The PURL for creator currently points to, which defines in human terms the DC creator property.

What is the advantage
What is the Advantage?

  • So far, properties are just conventional URI names.

    • All semantic web properties are conventional assertions about relationships between resources.

    • RDFS and DAML will offer more precise property capabilities.

  • But there is a powerful feature we are about to explore…

    • Properties provide a powerful way of linking different RDF resources

      • “Nuggets” of information.

  • For example, a publication is a resource that can be described by RDF

    • Author, publication date, URL are all metadata property values.

    • But publications have references that are just other publications

    • DC’s “hasReference” can be used to point from one publication to another.

  • Publication also have authors

    • An author is more than a name

    • Also an RDF resource with collections of properties

      • Name, email, telephone number,

Vcard representing people with rdf properties
vCard: Representing People with RDF Properties

  • The Dublin Core tags are best used to represent metadata about “published content”

    • Documents, published data

  • vCards are an IETF standard for representing people

    • Typical properties include name, email, organization membership, mailing address, title, etc.

    • See

  • Like the DC, vCards are independent of (and predate) RDF but are map naturally into RDF.

    • Each of these maps naturally to an RDF property

    • See

Example a vcard in rdf xml
Example: A vCard in RDF/XML

<rdf:RDF xmlns:rdf='' xmlns:vcard=''><rdf:Description rdf:about='' vcard:EMAIL=''> <vcard:FN>Geoffrey Fox</vcard:FN><vcard:N vcard:Given='Geoffrey' vcard:Family='Fox'/> </rdf:Description></rdf:RDF>

Linking vcard and dublin core resources
Linking vCard and Dublin Core Resources

  • The real power of RDF is that you can link two independently specified resources through the use of properties.

  • We do this using URIs as universal pointers

    • Identify specific resources (nouns) and specifications for properties (verbs)

    • The URIs may optionally be URLs that can be used to fetch the information.

  • Linking these resource nuggets allows us to pose queries like

    • “What is the email address of the creator of this entry in the chemical database?”

    • “What other entries reference directly or indirectly on this data entry?”

  • Linkages can be made at any time

    • Don’t have to be designed into the system

Graph model depicting vcard and dc linking
Graph Model Depicting vCard and DC Linking










Aside 3 making graphs
Aside #3: Making Graphs

  • IsaViz is a nice tool for authoring graphs “for real”.

  • Allows you to create and manipulate graphs, export the resulting RDF.

  • Graph on right is the vCard RDF from previous slide.

What else does rdf do
What Else Does RDF Do?

  • Collections: typically used as the object of an RDF statement

    • Bag: unordered collection of resources or literals.

    • Sequence: ordered collection or resources or literals.

    • Alternative: collection of resources or literals, from which only one value may be chosen

  • Model operations (DAML)

    • Union, Intersection, Difference

    • These operations build new RDF documents out of two other documents.

    • For example, several RDF documents can be merged with a sequence of “union” operations, eliminating redundant entries.

Other semantic markup activities

Other Semantic Markup Activities

A tour of other semantic markup language activities.

Other semantic markup languages
Other Semantic Markup Languages

  • RDF Schema (RDFS):

    • Provides formal definitions of RDF

    • Also provides language tools for writing more specialized languages.

    • We’ll examine in more detail.

  • DARPA Agent Markup Language (DAML):

    • DAML-OIL is the language component of the DAML project.

    • Defined using RDF/RDFS.

    • We’ll examine in more detail.

  • Ontology Inference Layer (OIL):

    • OIL language expressed in terms of RDF/RDFS.

    • The OIL project is sponsored by the European Union.

  • Web-Ontology Language (OWL):

    • Developed by the W3C’s Web-Ontology Working Group

    • Based on DAML-OIL

Rdf schema
RDF Schema

  • RDF Schema is a rules system for building RDF languages.

    • RDF and RDFS are defined in terms of RDFS

    • DAML+OIL is defined by RDFS.

  • Take the Dublin Core RDF encoding as an example:

    • Can we formalize this process, defining a consistent set of rules?

    • Can we place restrictions and use inheritance to define resources?

      • What really is the value of “creator”? Can I derive it from another class, like “person”?

    • Can we provide restrictions and rules for properties?

      • How can I express the fact that “title” should only appear once?

    • Current DC encoding in fact is defined by RDFS.

Rdfs class org chart
RDFS Class Org Chart






Instance Of:

Subclass Of:


Sample rdfs defining property
Sample RDFS: Defining <Property>

<rdfs:Class rdf:about="http://.../some/uri">

<rdfs:isDefinedBy rdf:resource="http://.../some/uri"/>


<rdfs:comment>The class of RDF properties.</rdfs:comment>

<rdfs:subClassOf rdf:resource="http://.../#Resource”>


  • This is the definition of <property>, taken from the RDF schema.

  • The “about” attribute labels names this nugget.

  • <property> has several properties

    • <label>,<comment> are self explanatory.

    • <subClassOf> means <property> is a subclass of <resource>

    • <isDefinedBy> points to the human-readable documentation.

Rdfs takeaway
RDFS Takeaway

  • RDFS defines a set of classes and properties that can be used to define new RDF-like languages.

    • RDFS actually bootstraps itself.

  • You can express inheritance, restriction

  • If you want to learn more, see the specification


  • But don’t trust the write up:

    • It has some errors

    • Concepts are best understood by looking at the RDF XML. English descriptions get convoluted.

  • If you want to see RDFS in action, see the DC:


What is daml oil
What is DAML-OIL?

  • RDFS is a pretty sparse

    • Meant to be extended into more useful languages.

  • Some missing features, summarized on next table.

  • DAML-OIL builds on RDF and RDFS to define several more useful properties and classes.

  • DAML-OIL is an assertive markup language for

    • describing resources (labels, comments, etc.)

    • expressing relationships between resources through properties

  • It is not a programming language.

    • Compliant DAML parsers and other tools must obey assertion rules.

What s an ontology
What’s an Ontology?

  • “Ontology” is an often used term in the field of Knowledge Representation, Information Modeling, etc.

  • English definitions tend to be vague to non-specialists

    • “A formal, explicit specification of a shared conceptionalization”

  • Clearer definition: an ontology is a taxonomy combined with inference rules

    • T. Berners-Lee, J. Hendler, O. Lassila

  • But really, if you sit down to describe a subject in terms of its classes and their relationships using RDFS or DAML, you are creating an Ontology.

    • See the HPCMP Ontology example in the report.

Philosophy s fine but can i program it
Philosophy’s Fine, but Can I Program It?

  • Yes. The HP Lab’s Jena package provide Java classes for creating programs to RDF/RDFS, DAML, and now OWL.

  • Several tools built on top of Jena

    • IsaViz, Protégé are two nice authoring tools.

  • Also tools for Perl, Python, C, Tcl/TK

    • See the W3C RDF web site.

More information
More Information

  • We have authored several survey reports.


    • See these reports for longer discussions, examples, and references.

  • For programming examples, see

    • Written in Java, using HPL’s Jena 1.6.1

Some miscellaneous slides

Some miscellaneous slides

Anticipating questions

What is the difference between the semantic web and databases
What is the Difference Between the Semantic Web and Databases?

  • Databases can certainly be used to store RDF, DAML, etc, entries.

    • Jena optionally stores RDF models in memory, relational DBs, or XML DBs (Berkeley Sleepy Cat).

  • Interesting comparison is between Semantic Web and Database Management Systems

    • Arguably these are two ends of same spectrum

    • DMS represent tight coupling of db software, storage, replication, data models, query services, etc.

      • One organization may control all data

    • SW appropriate for large, loosely coupled systems

      • No centralized control of data

      • Metadata may be directly embedded in the data, may be separate, may be scavenged by roving agents, ….

The semantic web and web services
The Semantic Web and Web Services Databases?

  • Web services that employ semantic data models can be built easily from packages like Jena.

    • Its really just an implementation choice.

  • A more interesting possibility is to describe Web services themselves in RDF(S), DAML, etc.

    • Discover a service that meets a problem description.

    • Link services that are compatible

  • DAML-S is one (DARPA-lead) concerted effort to do this.

  • “Semantic Grid” efforts in the Global Grid Forum are also examining this.